Partial Line Fittings for Classification

My problem now is to see how to fit partial lines. Originally, I wanted the trajectory creation to make a line like Fig 1. All incoming traffic will have a path that is similar to this path given that it is tracked fine..

Fig. 1 ideal case

The tracking system I had used is still vulnerable to mis-labeling. This is even after using minimum distance and area difference to retain a previous labeling across frames.

On Fig.1, we see a stolen trajectory from an opposing traffic, but it still possess the last portion which can be used to determine if it is incoming traffic. It still is able the same slope and direction movement as Fig. 1. So how can we just use the last part of the line instead of the whole trajectory path?

both min. dist and area difference for labeling:


Fig.1a,b Stolen trajectory from opposing traffic.


Fig. 2 Opposing traffic.


Harris Corner Detection
Curious to see how harris interest pts would work out. I used N. True's method for his parking example: Do a harris corner detect over the region of interest and sum up the points. The sum should different from empty traffic because pavement produces no corners. I've tried this with N. True's openCV implementation and it is able to pick up interest points on the bike.

The biggest problem I see is how occlusion of cross traffic can greatly affect it which is why motion can be used to help separate correct incoming traffic.